loading packages

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(sjPlot))
suppressPackageStartupMessages(library(lme4))
suppressPackageStartupMessages(library(performance))
suppressPackageStartupMessages(library(nortest))

options(scipen = 999)

importing dataset

dat_model <- read_csv("data/data_model.csv")

baseline model

# buliding model
baseline_model <-
  lmer(NA_sum ~ age + gender + beep + day + 
      event_unpleasantness_centered +
      (event_unpleasantness_centered | id),
      data = dat_model
  )


# model outputs
summary(baseline_model)

## Linear mixed model fit by REML ['lmerMod']
## Formula: NA_sum ~ age + gender + beep + day + event_unpleasantness_centered +  
##     (event_unpleasantness_centered | id)
##    Data: dat_model
## 
## REML criterion at convergence: 33534.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5677 -0.4675 -0.0862  0.2792  7.6403 
## 
## Random effects:
##  Groups   Name                          Variance Std.Dev. Corr
##  id       (Intercept)                   0.86360  0.9293       
##           event_unpleasantness_centered 0.03975  0.1994   0.63
##  Residual                               0.56096  0.7490       
## Number of obs: 14265, groups:  id, 247
## 
## Fixed effects:
##                                 Estimate Std. Error t value
## (Intercept)                    0.3938425  0.1654936   2.380
## age                           -0.0085250  0.0039792  -2.142
## genderMale                    -0.2610865  0.1350536  -1.933
## genderOther                   -0.1046100  0.7399324  -0.141
## beep                           0.0052154  0.0029603   1.762
## day                           -0.0047454  0.0008428  -5.631
## event_unpleasantness_centered  0.3095254  0.0150901  20.512
## 
## Correlation of Fixed Effects:
##             (Intr) age    gndrMl gndrOt beep   day   
## age         -0.912                                   
## genderMale  -0.093 -0.066                            
## genderOther -0.039  0.016  0.031                     
## beep        -0.076  0.002  0.000  0.001              
## day         -0.045  0.000  0.003 -0.003  0.008       
## evnt_nplsn_  0.202  0.001 -0.046  0.010  0.034  0.012

tab_model(baseline_model, show.std = T)

	NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.39	0.13	0.07 – 0.72	0.02 – 0.23	0.017
age	-0.01	-0.09	-0.02 – -0.00	-0.18 – -0.01	0.032
gender [Male]	-0.26	-0.21	-0.53 – 0.00	-0.42 – 0.00	0.053
gender [Other]	-0.10	-0.08	-1.55 – 1.35	-1.25 – 1.09	0.888
beep	0.01	0.01	-0.00 – 0.01	-0.00 – 0.02	0.078
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001
event unpleasantness centered	0.31	0.32	0.28 – 0.34	0.29 – 0.35	<0.001
Random Effects
σ²	0.56
τ₀₀ _id	0.86
τ₁₁ _{id.event_unpleasantness_centered}	0.04
ρ₀₁ _id	0.63
ICC	0.62
N _id	247
Observations	14265
Marginal R² / Conditional R²	0.111 / 0.665

# assumption checks
check_collinearity(baseline_model)

plot_model(baseline_model, type = "diag")

## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.5.3
## Current Matrix version is 1.4.1
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package

## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.1
## Current TMB version is 1.9.2
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)

## [[1]]

## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$id

## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]

## `geom_smooth()` using formula 'y ~ x'

#check_model(baseline_model)


# visualizing predicted values
plot_model(baseline_model)

# goodness of fit indicators
AIC_baseline <- AIC(baseline_model)
BIC_baseline <- BIC(baseline_model)


# normality test for residuals
baseline_model_residuals <- residuals(baseline_model)
lillie.test(baseline_model_residuals)

## 
##  Lilliefors (Kolmogorov-Smirnov) normality test
## 
## data:  baseline_model_residuals
## D = 0.14284, p-value < 0.00000000000000022

# normality test for NA_sum
lillie.test(dat_model$NA_sum)

## 
##  Lilliefors (Kolmogorov-Smirnov) normality test
## 
## data:  dat_model$NA_sum
## D = 0.21912, p-value < 0.00000000000000022

trait rumination

# building model
trait_model <-
  lmer(
    NA_sum ~ age + gender + beep + day +
    event_unpleasantness_centered * trait_rumination +
    (event_unpleasantness_centered | id),
      data = dat_model
    )


# model outputs
summary(trait_model)

## Linear mixed model fit by REML ['lmerMod']
## Formula: NA_sum ~ age + gender + beep + day + event_unpleasantness_centered *  
##     trait_rumination + (event_unpleasantness_centered | id)
##    Data: dat_model
## 
## REML criterion at convergence: 33508.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5777 -0.4677 -0.0848  0.2800  7.6410 
## 
## Random effects:
##  Groups   Name                          Variance Std.Dev. Corr
##  id       (Intercept)                   0.77855  0.8824       
##           event_unpleasantness_centered 0.03429  0.1852   0.59
##  Residual                               0.56096  0.7490       
## Number of obs: 14265, groups:  id, 247
## 
## Fixed effects:
##                                                  Estimate Std. Error t value
## (Intercept)                                     0.2160764  0.1728315   1.250
## age                                            -0.0047372  0.0041325  -1.146
## genderMale                                     -0.1152025  0.1390809  -0.828
## genderOther                                     0.0497428  0.7339194   0.068
## beep                                            0.0051633  0.0029599   1.744
## day                                            -0.0047750  0.0008425  -5.668
## event_unpleasantness_centered                   0.3053063  0.0143028  21.346
## trait_rumination                                0.3551413  0.0664066   5.348
## event_unpleasantness_centered:trait_rumination  0.0779311  0.0150512   5.178
## 
## Correlation of Fixed Effects:
##             (Intr) age    gndrMl gndrOt beep   day    evnt__ trt_rm
## age         -0.925                                                 
## genderMale  -0.190  0.040                                          
## genderOther -0.071  0.047  0.059                                   
## beep        -0.069 -0.001 -0.002  0.000                            
## day         -0.041 -0.002  0.002 -0.003  0.008                     
## evnt_nplsn_  0.159  0.011 -0.036  0.013  0.035  0.011              
## trait_rmntn -0.308  0.281  0.289  0.082 -0.006 -0.005 -0.014       
## evnt_npl_:_  0.010 -0.018  0.030 -0.015  0.003 -0.003 -0.078  0.457

tab_model(trait_model, show.std = T)

	NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p	std. p
(Intercept)	0.22	0.11	-0.12 – 0.55	0.01 – 0.21	0.211	0.033
age	-0.00	-0.05	-0.01 – 0.00	-0.14 – 0.04	0.252	0.252
gender [Male]	-0.12	-0.09	-0.39 – 0.16	-0.31 – 0.13	0.408	0.408
gender [Other]	0.05	0.04	-1.39 – 1.49	-1.12 – 1.20	0.946	0.946
beep	0.01	0.01	-0.00 – 0.01	-0.00 – 0.02	0.081	0.081
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001	<0.001
event unpleasantness centered	0.31	0.32	0.28 – 0.33	0.29 – 0.35	<0.001	<0.001
trait rumination	0.36	0.28	0.22 – 0.49	0.18 – 0.38	<0.001	<0.001
event unpleasantness centered × trait rumination	0.08	0.08	0.05 – 0.11	0.05 – 0.11	<0.001	<0.001
Random Effects
σ²	0.56
τ₀₀ _id	0.78
τ₁₁ _{id.event_unpleasantness_centered}	0.03
ρ₀₁ _id	0.59
ICC	0.60
N _id	247
Observations	14265
Marginal R² / Conditional R²	0.188 / 0.674

# assumption checks
check_collinearity(trait_model)

plot_model(trait_model, type = "diag")

## [[1]]

## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$id

## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]

## `geom_smooth()` using formula 'y ~ x'

#check_model(trait_model)


# visualizing predicted values
plot_model(trait_model)

# goodness of fit indicators
AIC_trait <- AIC(trait_model)
BIC_trait <- BIC(trait_model)

coefplot for trait rumination

coefplot_trait <- 
  plot_model(trait_model,
             type = "std",
             rm.terms = c("age", "gender [Male]", "gender [Other]", "beep", "day", "event_unpleasantness_centered"),
             ci.lvl = 0.95,
             std.est = T,
             title = "",
             show.values = TRUE,
             colors = "black",
             axis.labels = c("Trait rumination * Perceived stress", "Trait rumination"),
             axis.title = "Standardized β coefficients"
             ) +
             ylim(0, 0.6) +
             theme_minimal() +
             theme(axis.line = element_line(size = .3),
                   axis.text = element_text(size = 10, color = "black"),
                   axis.title.x = element_text(size = 12, color = "black")
                   )

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

coefplot_trait

mean state rumination

# building model
mean_state_model <-
  lmer(
    NA_sum ~ age + gender + beep + day +
    event_unpleasantness_centered * mean_state_rumi_grand_centered +
    (event_unpleasantness_centered | id),
    data = dat_model
    )

# model outputs
summary(mean_state_model)

## Linear mixed model fit by REML ['lmerMod']
## Formula: NA_sum ~ age + gender + beep + day + event_unpleasantness_centered *  
##     mean_state_rumi_grand_centered + (event_unpleasantness_centered |      id)
##    Data: dat_model
## 
## REML criterion at convergence: 33378.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5871 -0.4680 -0.0864  0.2809  7.6518 
## 
## Random effects:
##  Groups   Name                          Variance Std.Dev. Corr
##  id       (Intercept)                   0.42164  0.6493       
##           event_unpleasantness_centered 0.03091  0.1758   0.51
##  Residual                               0.56109  0.7491       
## Number of obs: 14265, groups:  id, 247
## 
## Fixed effects:
##                                                                Estimate
## (Intercept)                                                   0.1402751
## age                                                          -0.0032922
## genderMale                                                   -0.0501490
## genderOther                                                   0.1741375
## beep                                                          0.0049676
## day                                                          -0.0048631
## event_unpleasantness_centered                                 0.3068529
## mean_state_rumi_grand_centered                                0.9105524
## event_unpleasantness_centered:mean_state_rumi_grand_centered  0.1227505
##                                                              Std. Error t value
## (Intercept)                                                   0.1291530   1.086
## age                                                           0.0031130  -1.058
## genderMale                                                    0.1062399  -0.472
## genderOther                                                   0.5730614   0.304
## beep                                                          0.0029591   1.679
## day                                                           0.0008409  -5.783
## event_unpleasantness_centered                                 0.0138163  22.209
## mean_state_rumi_grand_centered                                0.0598964  15.202
## event_unpleasantness_centered:mean_state_rumi_grand_centered  0.0188286   6.519
## 
## Correlation of Fixed Effects:
##             (Intr) age    gndrMl gndrOt beep   day    evnt__ mn____
## age         -0.922                                                 
## genderMale  -0.129 -0.027                                          
## genderOther -0.053  0.028  0.045                                   
## beep        -0.095 -0.001 -0.002  0.001                            
## day         -0.055 -0.004  0.005 -0.004  0.007                     
## evnt_nplsn_  0.139  0.005 -0.033  0.010  0.036  0.011              
## mn_stt_rm__ -0.182  0.155  0.200  0.061 -0.009 -0.001 -0.015       
## evnt__:____  0.016 -0.021  0.001 -0.012  0.004 -0.002 -0.063  0.387

tab_model(mean_state_model, show.std = T)

	NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.14	0.09	-0.11 – 0.39	0.01 – 0.16	0.277
age	-0.00	-0.04	-0.01 – 0.00	-0.10 – 0.03	0.290
gender [Male]	-0.05	-0.04	-0.26 – 0.16	-0.21 – 0.13	0.637
gender [Other]	0.17	0.14	-0.95 – 1.30	-0.77 – 1.05	0.761
beep	0.00	0.01	-0.00 – 0.01	-0.00 – 0.02	0.093
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001
event unpleasantness centered	0.31	0.32	0.28 – 0.33	0.29 – 0.35	<0.001
mean state rumi grand centered	0.91	0.52	0.79 – 1.03	0.45 – 0.58	<0.001
event unpleasantness centered × mean state rumi grand centered	0.12	0.09	0.09 – 0.16	0.06 – 0.12	<0.001
Random Effects
σ²	0.56
τ₀₀ _id	0.42
τ₁₁ _{id.event_unpleasantness_centered}	0.03
ρ₀₁ _id	0.51
ICC	0.46
N _id	247
Observations	14265
Marginal R² / Conditional R²	0.372 / 0.659

# assumption checks
check_collinearity(mean_state_model)

plot_model(mean_state_model, type = "diag")

## [[1]]

## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$id

## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]

## `geom_smooth()` using formula 'y ~ x'

#check_model(mean_state_model)


# visualizing predicted values
plot_model(mean_state_model)

# goodness of fit indicators
AIC_mean_state <- AIC(mean_state_model)
BIC_mean_state <- BIC(mean_state_model)

coefplot for mean state rumination

coefplot_mean_state <- 
  plot_model(mean_state_model,
             type = "std",
             rm.terms = c("age", "gender [Male]", "gender [Other]", "beep", "day", "event_unpleasantness_centered"),
             ci.lvl = 0.95,
             std.est = T,
             title = "",
             show.values = TRUE,
             colors = "black",
             axis.labels = c("Mean state rumination * Perceived stress", "Mean state rumination"),
             axis.title = "Standardized β coefficients"
             ) +
             ylim(0, 0.6) +
             theme_minimal() +
             theme(axis.line = element_line(size = .3),
                   axis.text = element_text(size = 10, color = "black"),
                   axis.title.x = element_text(size = 12, color = "black")
                   )

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

coefplot_mean_state

state rumination

# building model
state_model <-
  lmer(
    NA_sum ~ age + gender + beep + day +
    event_unpleasantness_centered * rumination_centered +
    (event_unpleasantness_centered | id), data = dat_model
    )


# model outputs
summary(state_model)

## Linear mixed model fit by REML ['lmerMod']
## Formula: NA_sum ~ age + gender + beep + day + event_unpleasantness_centered *  
##     rumination_centered + (event_unpleasantness_centered | id)
##    Data: dat_model
## 
## REML criterion at convergence: 31593.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5625 -0.4499 -0.0771  0.2846  8.2712 
## 
## Random effects:
##  Groups   Name                          Variance Std.Dev. Corr
##  id       (Intercept)                   0.86414  0.9296       
##           event_unpleasantness_centered 0.02793  0.1671   0.66
##  Residual                               0.48929  0.6995       
## Number of obs: 14265, groups:  id, 247
## 
## Fixed effects:
##                                                     Estimate Std. Error t value
## (Intercept)                                        0.4238103  0.1637515   2.588
## age                                               -0.0090868  0.0039334  -2.310
## genderMale                                        -0.2758397  0.1338196  -2.061
## genderOther                                       -0.1560593  0.7273910  -0.215
## beep                                               0.0009888  0.0027650   0.358
## day                                               -0.0045018  0.0007871  -5.720
## event_unpleasantness_centered                      0.2545536  0.0129058  19.724
## rumination_centered                                0.2651076  0.0066691  39.751
## event_unpleasantness_centered:rumination_centered  0.0386275  0.0038579  10.013
## 
## Correlation of Fixed Effects:
##             (Intr) age    gndrMl gndrOt beep   day    evnt__ rmntn_
## age         -0.912                                                 
## genderMale  -0.094 -0.066                                          
## genderOther -0.039  0.016  0.031                                   
## beep        -0.072  0.002  0.001  0.001                            
## day         -0.043  0.001  0.002 -0.002  0.008                     
## evnt_nplsn_  0.206  0.003 -0.050  0.012  0.040  0.011              
## rmntn_cntrd  0.015 -0.009 -0.014 -0.005 -0.029 -0.005 -0.076       
## evnt_npl_:_ -0.009  0.001  0.006 -0.001 -0.005  0.022 -0.031 -0.299

tab_model(state_model, show.std = T)

	NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.42	0.12	0.10 – 0.74	0.02 – 0.23	0.010
age	-0.01	-0.10	-0.02 – -0.00	-0.19 – -0.02	0.021
gender [Male]	-0.28	-0.22	-0.54 – -0.01	-0.43 – -0.01	0.039
gender [Other]	-0.16	-0.13	-1.58 – 1.27	-1.28 – 1.02	0.830
beep	0.00	0.00	-0.00 – 0.01	-0.01 – 0.01	0.721
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001
event unpleasantness centered	0.25	0.27	0.23 – 0.28	0.24 – 0.29	<0.001
rumination centered	0.27	0.21	0.25 – 0.28	0.20 – 0.22	<0.001
event unpleasantness centered × rumination centered	0.04	0.04	0.03 – 0.05	0.03 – 0.05	<0.001
Random Effects
σ²	0.49
τ₀₀ _id	0.86
τ₁₁ _{id.event_unpleasantness_centered}	0.03
ρ₀₁ _id	0.66
ICC	0.65
N _id	247
Observations	14265
Marginal R² / Conditional R²	0.164 / 0.708

# assumption checks
check_collinearity(state_model)

plot_model(state_model, type = "diag")

## [[1]]

## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$id

## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]

## `geom_smooth()` using formula 'y ~ x'

#check_model(state_model)


# visualizing predicted values
plot_model(state_model)

# goodness of fit indicators
AIC_state <- AIC(state_model)
BIC_state <- BIC(state_model)



# outputs for trait, mean state and state models
tab_model(trait_model, mean_state_model, state_model, show.std = T)

	NA sum						NA sum					NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p	std. p	Estimates	std. Beta	CI	standardized CI	p	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.22	0.11	-0.12 – 0.55	0.01 – 0.21	0.211	0.033	0.14	0.09	-0.11 – 0.39	0.01 – 0.16	0.277	0.42	0.12	0.10 – 0.74	0.02 – 0.23	0.010
age	-0.00	-0.05	-0.01 – 0.00	-0.14 – 0.04	0.252	0.252	-0.00	-0.04	-0.01 – 0.00	-0.10 – 0.03	0.290	-0.01	-0.10	-0.02 – -0.00	-0.19 – -0.02	0.021
gender [Male]	-0.12	-0.09	-0.39 – 0.16	-0.31 – 0.13	0.408	0.408	-0.05	-0.04	-0.26 – 0.16	-0.21 – 0.13	0.637	-0.28	-0.22	-0.54 – -0.01	-0.43 – -0.01	0.039
gender [Other]	0.05	0.04	-1.39 – 1.49	-1.12 – 1.20	0.946	0.946	0.17	0.14	-0.95 – 1.30	-0.77 – 1.05	0.761	-0.16	-0.13	-1.58 – 1.27	-1.28 – 1.02	0.830
beep	0.01	0.01	-0.00 – 0.01	-0.00 – 0.02	0.081	0.081	0.00	0.01	-0.00 – 0.01	-0.00 – 0.02	0.093	0.00	0.00	-0.00 – 0.01	-0.01 – 0.01	0.721
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001	<0.001	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001
event unpleasantness centered	0.31	0.32	0.28 – 0.33	0.29 – 0.35	<0.001	<0.001	0.31	0.32	0.28 – 0.33	0.29 – 0.35	<0.001	0.25	0.27	0.23 – 0.28	0.24 – 0.29	<0.001
trait rumination	0.36	0.28	0.22 – 0.49	0.18 – 0.38	<0.001	<0.001
event unpleasantness centered × trait rumination	0.08	0.08	0.05 – 0.11	0.05 – 0.11	<0.001	<0.001
mean state rumi grand centered							0.91	0.52	0.79 – 1.03	0.45 – 0.58	<0.001
event unpleasantness centered × mean state rumi grand centered							0.12	0.09	0.09 – 0.16	0.06 – 0.12	<0.001
rumination centered												0.27	0.21	0.25 – 0.28	0.20 – 0.22	<0.001
event unpleasantness centered × rumination centered												0.04	0.04	0.03 – 0.05	0.03 – 0.05	<0.001
Random Effects
σ²	0.56						0.56					0.49
τ₀₀	0.78 _id						0.42 _id					0.86 _id
τ₁₁	0.03 _{id.event_unpleasantness_centered}						0.03 _{id.event_unpleasantness_centered}					0.03 _{id.event_unpleasantness_centered}
ρ₀₁	0.59 _id						0.51 _id					0.66 _id
ICC	0.60						0.46					0.65
N	247 _id						247 _id					247 _id
Observations	14265						14265					14265
Marginal R² / Conditional R²	0.188 / 0.674						0.372 / 0.659					0.164 / 0.708

coefplot for state rumination

coefplot_state <- 
  plot_model(state_model,
             type = "std",
             rm.terms = c("age", "gender [Male]", "gender [Other]", "beep", "day", "event_unpleasantness_centered"),
             ci.lvl = 0.95,
             std.est = T,
             title = "",
             show.values = TRUE,
             colors = "black",
             axis.labels = c("State rumination * Perceived stress", "State rumination"),
             axis.title = "Standardized β coefficients"
             ) +
             ylim(0, 0.6) +
             theme_minimal() +
             theme(axis.line = element_line(size = .3),
                   axis.text = element_text(size = 10, color = "black"),
                   axis.title.x = element_text(size = 12, color = "black")
                   )

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

coefplot_state

full model

# building model
full_model <-
  
  lmer(
    NA_sum ~ age + gender + beep + day +
    event_unpleasantness_centered * rumination_centered +  
    event_unpleasantness_centered * mean_state_rumi_grand_centered +
    event_unpleasantness_centered * trait_rumination +
      
    (event_unpleasantness_centered * rumination_centered | id), data = dat_model,
    control = lmerControl(optCtrl=list(ftol_abs=1e-8,xtol_abs=1e-8))
    )

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00632854 (tol = 0.002, component 1)

# model outputs
summary(full_model)

## Linear mixed model fit by REML ['lmerMod']
## Formula: NA_sum ~ age + gender + beep + day + event_unpleasantness_centered *  
##     rumination_centered + event_unpleasantness_centered * mean_state_rumi_grand_centered +  
##     event_unpleasantness_centered * trait_rumination + (event_unpleasantness_centered *  
##     rumination_centered | id)
##    Data: dat_model
## Control: 
## lmerControl(optCtrl = list(ftol_abs = 0.00000001, xtol_abs = 0.00000001))
## 
## REML criterion at convergence: 30831.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8435 -0.4461 -0.0838  0.2763  8.6704 
## 
## Random effects:
##  Groups   Name                                              Variance Std.Dev.
##  id       (Intercept)                                       0.433417 0.65834 
##           event_unpleasantness_centered                     0.020676 0.14379 
##           rumination_centered                               0.030910 0.17581 
##           event_unpleasantness_centered:rumination_centered 0.004586 0.06772 
##  Residual                                                   0.457525 0.67641 
##  Corr             
##                   
##   0.58            
##   0.37  0.03      
##  -0.21 -0.09 -0.31
##                   
## Number of obs: 14265, groups:  id, 247
## 
## Fixed effects:
##                                                                Estimate
## (Intercept)                                                   0.0756341
## age                                                          -0.0020003
## genderMale                                                    0.0240987
## genderOther                                                   0.1904986
## beep                                                          0.0006876
## day                                                          -0.0039193
## event_unpleasantness_centered                                 0.2476894
## rumination_centered                                           0.2753280
## mean_state_rumi_grand_centered                                0.8390301
## trait_rumination                                              0.1332307
## event_unpleasantness_centered:rumination_centered             0.0317497
## event_unpleasantness_centered:mean_state_rumi_grand_centered  0.0616133
## event_unpleasantness_centered:trait_rumination                0.0411148
##                                                              Std. Error t value
## (Intercept)                                                   0.1272319   0.594
## age                                                           0.0030174  -0.663
## genderMale                                                    0.1038705   0.232
## genderOther                                                   0.5471433   0.348
## beep                                                          0.0026971   0.255
## day                                                           0.0007744  -5.061
## event_unpleasantness_centered                                 0.0116109  21.333
## rumination_centered                                           0.0167922  16.396
## mean_state_rumi_grand_centered                                0.0609793  13.759
## trait_rumination                                              0.0513502   2.595
## event_unpleasantness_centered:rumination_centered             0.0078451   4.047
## event_unpleasantness_centered:mean_state_rumi_grand_centered  0.0170833   3.607
## event_unpleasantness_centered:trait_rumination                0.0131559   3.125

## 
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00632854 (tol = 0.002, component 1)

tab_model(full_model, show.std = T)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00403381 (tol = 0.002, component 1)

	NA sum
Predictors	Estimates	std. Beta	CI	standardized CI	p	std. p
(Intercept)	0.08	0.07	-0.17 – 0.33	-0.00 – 0.15	0.552	0.057
age	-0.00	-0.02	-0.01 – 0.00	-0.09 – 0.04	0.507	0.508
gender [Male]	0.02	0.02	-0.18 – 0.23	-0.14 – 0.18	0.817	0.816
gender [Other]	0.19	0.15	-0.88 – 1.26	-0.71 – 1.02	0.728	0.728
beep	0.00	0.00	-0.00 – 0.01	-0.01 – 0.01	0.799	0.799
day	-0.00	-0.03	-0.01 – -0.00	-0.04 – -0.02	<0.001	<0.001
event unpleasantness centered	0.25	0.26	0.22 – 0.27	0.24 – 0.28	<0.001	<0.001
rumination centered	0.28	0.22	0.24 – 0.31	0.19 – 0.24	<0.001	<0.001
mean state rumi grand centered	0.84	0.48	0.72 – 0.96	0.41 – 0.54	<0.001	<0.001
trait rumination	0.13	0.10	0.03 – 0.23	0.03 – 0.18	0.009	0.009
event unpleasantness centered × rumination centered	0.03	0.03	0.02 – 0.05	0.02 – 0.05	<0.001	<0.001
event unpleasantness centered × mean state rumi grand centered	0.06	0.05	0.03 – 0.10	0.02 – 0.07	<0.001	<0.001
event unpleasantness centered × trait rumination	0.04	0.04	0.02 – 0.07	0.02 – 0.07	0.002	0.002
Random Effects
σ²	0.46
τ₀₀ _id	0.43
τ₁₁ _{id.event_unpleasantness_centered}	0.02
τ₁₁ _{id.rumination_centered}	0.03
τ₁₁ _{id.event_unpleasantness_centered:rumination_centered}	0.00
ρ₀₁	0.58
	0.37
	-0.21
ICC	0.52
N _id	247
Observations	14265
Marginal R² / Conditional R²	0.424 / 0.725

# assumption checks
check_collinearity(full_model)

plot_model(state_model, type = "diag")

## [[1]]

## `geom_smooth()` using formula 'y ~ x'

## 
## [[2]]
## [[2]]$id

## `geom_smooth()` using formula 'y ~ x'

## 
## 
## [[3]]

## 
## [[4]]

## `geom_smooth()` using formula 'y ~ x'

#check_model(full_model)


# visualizing predicted values
plot_model(full_model)

# goodness of fit indicators
AIC_full <- AIC(full_model)
BIC_full <- BIC(full_model)

coefplots for full model

# plot for interactions (colored afterwards in photoshop)
coefplot_interactions <- 
  plot_model(full_model,
             type = "std",
             rm.terms = c("rumination_centered", "trait_rumination", "mean_state_rumi_grand_centered", "age", "gender [Male]", "gender [Other]",   "beep", "day", "event_unpleasantness_centered"),
             ci.lvl = 0.95,
             std.est = T,
             title = "",
             show.values = TRUE,
             colors = "black",
             axis.labels = c("Trait rumination * Perceived stress", "Mean state rumination * Perceived stress", "State rumination * Perceived stress"),
             axis.title = "Standardized β coefficients"
             ) +
             ylim(0, 0.1) +
             theme_minimal() +
             theme(axis.text = element_text(size = 10, color = "black"),
                   axis.title.x = element_text(size = 12, color = "black"),
                   axis.line = element_line(size = .3)
                   )

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00403381 (tol = 0.002, component 1)

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

coefplot_interactions

# plot for main effects (colored afterwards in photoshop)
coefplot_maineffects <- 
  plot_model(full_model,
             type = "std",
             terms = c("rumination_centered", "mean_state_rumi_grand_centered", "trait_rumination"),
             ci.lvl = 0.95,
             std.est = TRUE,
             title = "",
             show.values = TRUE,
             colors = "black",
             axis.labels = c("Trait rumination", "Mean state rumination", "State rumination"),
             axis.title = "Standardized β coefficients"
             ) +
             ylim(0, 1) +
             theme_minimal() +
             theme(axis.text = element_text(size = 10, color = "black"),
                   axis.title.x = element_text(size = 12, color = "black"),
                   axis.line = element_line(size = .3)
                   )

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00403381 (tol = 0.002, component 1)

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

coefplot_maineffects

saving full model for bootstrapping

saveRDS(full_model, "saved_models/full_model.rds")

03_models

Flora

loading packages

importing dataset

baseline model

trait rumination

coefplot for trait rumination

mean state rumination

coefplot for mean state rumination

state rumination

coefplot for state rumination

full model

coefplots for full model

saving full model for bootstrapping